DS004477#

PES - Pandemic Emergency Scenario

Access recordings and metadata through EEGDash.

Citation: Tasos Papastylianou, Rodrigo Ramele, Luca Citi, Caterina Cinel, Riccardo Poli (2023). PES - Pandemic Emergency Scenario. 10.18112/openneuro.ds004477.v1.0.2

Modality: eeg Subjects: 9 Recordings: 68 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004477

dataset = DS004477(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS004477(cache_dir="./data", subject="01")

Advanced query

dataset = DS004477(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds004477,
  title = {PES - Pandemic Emergency Scenario},
  author = {Tasos Papastylianou and Rodrigo Ramele and Luca Citi and Caterina Cinel and Riccardo Poli},
  doi = {10.18112/openneuro.ds004477.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004477.v1.0.2},
}

About This Dataset#

Experiment:

PES is a complex and strategic decision-making “Pandemic” Experiment. In this experiment, users were shown a map that gives a description of the spread of a pandemic emergency situation in various locations within the map. Resources (in terms, medicines, personnels) are allocated to few cities in the beginning. The user must allocate more resources to new cities that are displayed on the map. The user must keep in mind that the resources are limited and handing over all resources could mean that new cities (if displayed) might not get any resources.

In this experiment, 9 participants are paired with an artificial agent and they have to decide resource allocation on this scenario, providing their reported confidences for each decision. The experiment is divided in 64 sequences.

Neurophysiological markers and behavioural information is obtained for each participant as they provide the number of allocated resources and their own subjective perception of the accuracy of each response for each trial. There is a span of 10 seconds where the Participant can press the mouse button (the Hold Response event), drag the mouse upwards while keeping the mouse-button pressed, thereby increasing the number of plus symbols that appear around the city icon, or downwards to decrease them, and finally release the mouse button when the decision is made (the Release Response event). Immediately after that, there is an additional span of 5 seconds where the participant reports the confidence in their decision by moving the mouse wheel. After that (the End-of-trial event) a black screen replaces the map, and the responses from the other players are shown for 2 seconds. Each participant sat comfortably at about 1 meter from an LCD monitor; each participant wore an EEG cap connected to a Biosemi ActiveTwo system. Wet electrodes were used and recordings were performed with 64 electrodes in the International 10-20 System. Eight additional external channels were also included, two measuring the electrocardiogram (ECG), while 4 measured the electrooculogram (EOG) signal. The EEG data was sampled at 2048 Hz.

Ethical Statement:

The study complied at all times with the Declaration of Helsinki ethical guidelines for research involving human subjects; formal ethical approval was granted by the Ministry of Defence Research Ethics Committee MoDREC – Application No: 983/MoDREC/19 first approved on 5th September 2019, with revisions (ver. 3) approved on the 3rd of June 2021.

Acknowledgment:

This research was supported by the Defence Science and Technology Laboratory (Dstl) on behalf of the UK Ministry of Defence (MOD) via funding from US/UK DoD Bilateral Academic Research Initiative (BARI).

Code: BCI-NE/PES

Dataset Information#

Dataset ID	`DS004477`
Title	PES - Pandemic Emergency Scenario
Year	2023
Authors	Tasos Papastylianou, Rodrigo Ramele, Luca Citi, Caterina Cinel, Riccardo Poli
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004477.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 9
Recordings: 68
Tasks: 1

Channels & sampling rate

Channels: 79 (9), 80 (9)
Sampling rate (Hz): 2048.0
Duration (hours): 0.0

API Reference#

Use the DS004477 class to access this dataset programmatically.

class eegdash.dataset.DS004477(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004477. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004477 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004477

Examples

>>> from eegdash.dataset import DS004477
>>> dataset = DS004477(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None